@article{delgado-licona_addington_alsaiari_abolhasani_2025, title={Engineering principles for self-driving laboratories}, volume={2}, url={https://doi.org/10.1038/s44286-025-00217-7}, DOI={10.1038/s44286-025-00217-7}, number={5}, journal={Nature Chemical Engineering}, author={Delgado-Licona, Fernando and Addington, Daniel and Alsaiari, Abdulrahman and Abolhasani, Milad}, year={2025}, month={May}, pages={277–280} }
 @article{delgado-licona_alsaiari_dickerson_klem_ghorai_canty_bennett_jha_mukhin_li_et al._2025, title={Flow-driven data intensification to accelerate autonomous inorganic materials discovery}, volume={2}, DOI={10.1038/s44286-025-00249-z}, number={7}, journal={Nature Chemical Engineering}, author={Delgado-Licona, Fernando and Alsaiari, Abdulrahman and Dickerson, Hannah and Klem, Philip and Ghorai, Arup and Canty, Richard B. and Bennett, Jeffrey A. and Jha, Pragyan and Mukhin, Nikolai and Li, Junbin and et al.}, year={2025}, month={Jul}, pages={436–446} }
 @article{jha_mukhin_ghorai_morshedian_canty_delgado‐licona_brown_pyrch_castellano_abolhasani_2025, title={Photo‐Induced Bandgap Engineering of Metal Halide Perovskite Quantum Dots In Flow}, volume={2}, url={https://doi.org/10.1002/adma.202419668}, DOI={10.1002/adma.202419668}, abstractNote={Over the past decade, lead halide perovskite (LHP) nanocrystals (NCs) have attracted significant attention due to their tunable optoelectronic properties for next-generation printed photonic and electronic devices. High-energy photons in the presence of haloalkanes provide a scalable and sustainable pathway for precise bandgap engineering of LHP NCs via photo-induced anion exchange reaction (PIAER) facilitated by in situ generated halide anions. However, the mechanisms driving photo-induced bandgap engineering in LHP NCs remain not fully understood. This study elucidates the underlying PIAER mechanisms of LHP NCs through an advanced microfluidic platform. Additionally, the first instance of a PIAER, transforming CsPbBr<sub>3</sub> NCs into high-performing CsPbI<sub>3</sub> NCs, with the assistance of a thiol-based additive is reported. Utilizing an intensified photo-flow microreactor accelerates the anion exchange rate 3.5-fold, reducing material consumption 100-fold compared to conventional batch processes. It is demonstrated that CsPbBr<sub>3</sub> NCs act as photocatalysts, driving oxidative bond cleavage in dichloromethane and promoting the photodissociation of 1-iodopropane using high-energy photons. Furthermore, it is demonstrated that a thiol-based additive plays a dual role: surface passivation, which enhances the photoluminescence quantum yield, and facilitates the PIAER. These findings pave the way for the tailored design of perovskite-based optoelectronic materials.}, journal={Advanced Materials}, author={Jha, Pragyan and Mukhin, Nikolai and Ghorai, Arup and Morshedian, Hamed and Canty, Richard B. and Delgado‐Licona, Fernando and Brown, Emily E. and Pyrch, Austin J. and Castellano, Felix N. and Abolhasani, Milad}, year={2025}, month={Feb} }
 @article{sadeghi_canty_mukhin_xu_delgado-licona_abolhasani_2024, title={Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories}, volume={8}, url={https://doi.org/10.1021/acssuschemeng.4c02177}, DOI={10.1021/acssuschemeng.4c02177}, abstractNote={The accelerating depletion of natural resources undoubtedly demands a radical reevaluation of research practices addressing the escalating climate crisis. From traditional approaches to modern-day advancements, the integration of automation and artificial intelligence (AI)-guided decision-making has emerged as a transformative route in shaping new research methodologies. Harnessing robotics and high-throughput automation alongside intelligent experimental design, self-driving laboratories (SDLs) offer an innovative solution to expedite chemical/materials research timelines while significantly reducing the carbon footprint of scientific endeavors, which could be utilized to not only generate green materials but also make the research process itself more sustainable. In this Perspective, we examine the potential of SDLs in driving sustainability forward through case studies in materials discovery and process optimization, thereby paving the way for a greener and more efficient future. While SDLs hold an immense promise, we discuss the challenges that persist in their development and deployment, necessitating a holistic approach to sustainability in both design and implementation.}, journal={ACS Sustainable Chemistry & Engineering}, author={Sadeghi, Sina and Canty, Richard B. and Mukhin, Nikolai and Xu, Jinge and Delgado-Licona, Fernando and Abolhasani, Milad}, year={2024}, month={Aug} }
 @article{lópez-guajardo_galluzzi_delgado-licona_morales-menendez_2024, title={Process intensification of a catalytic-wall Taylor-Couette reactor through unconventional modulation of its angular speed}, DOI={10.1016/j.cej.2024.151174}, abstractNote={This study proposes a non-conventional operation of a Taylor-Couette reactor by introducing a periodic flow perturbation as a modulated angular velocity of its inner cylinder. The applicability of this type of waveform has yet to be explored in Taylor-Couette reactors as a means to enhance mass transfer phenomena in reactive systems. A multiphysics numerical study was carried out considering a mass-transfer limited system with a catalytic reaction at the outer cylinder boundary of the reactor while applying different modulations of the inner cylinder angular speed. Results showed that a modular signal could yield conversions similar to a constant angular-speed operation. However, the use of modulating signals brings two essential benefits. First, it enhances mass transfer, which yields higher conversions by dynamically changing the flow patterns in the reactor. This improvement is demonstrated and discussed in terms of a dynamic mixing index, which accounts for the formation and abrupt disruption of Taylor vortices in the reactor. Second and more importantly, this type of operation leads to an overall reduction in the electrical power required to drive the system (∼25 % reduction). The present study opens the possibility of using intelligent control strategies to optimize reactions and intensify conventional systems with non-conventional operation modes.}, journal={Chemical Engineering Journal}, author={López-Guajardo, Enrique A. and Galluzzi, Renato and Delgado-Licona, Fernando and Morales-Menendez, Ruben}, year={2024}, month={Apr} }
 @article{epps_delgado‐licona_yang_kim_volk_han_jun_abolhasani_2023, title={Accelerated Multi‐Stage Synthesis of Indium Phosphide Quantum Dots in Modular Flow Reactors}, volume={1}, url={https://doi.org/10.1002/admt.202201845}, DOI={10.1002/admt.202201845}, abstractNote={Abstract Development and scalable nanomanufacturing of high‐quality heavy metal‐free quantum dots (QDs) with high‐dimensional experimental design spaces still remain a challenge. In this work, a universal flow chemistry framework for accelerated fundamental and applied studies of heavy metal‐free QDs with multi‐stage chemistries is presented. By introducing flexible time‐ and temperature‐to‐distance transformation using modular fluidic blocks, an in‐flow synthetic route of InP QDs with the highest reported first excitonic absorption peak to valley ratio is unveiled with a reaction time one order of magnitude faster than batch reactors. The flexible time‐ and temperature‐to‐distance transformation as an enabling factor for generalization of flow reactors toward the accelerated discovery, development, and nanomanufacturing of high‐quality emerging nanomaterials for next‐generation energy, display, and chemical technologies is discussed.}, journal={Advanced Materials Technologies}, author={Epps, Robert W. and Delgado‐Licona, Fernando and Yang, Hyeyeon and Kim, Taekhoon and Volk, Amanda A. and Han, Suyong and Jun, Shinae and Abolhasani, Milad}, year={2023}, month={Jan} }
 @article{sadeghi_bateni_kim_son_bennett_orouji_punati_stark_cerra_awad_et al._2023, title={Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab}, volume={12}, DOI={10.1039/d3nr05034c}, abstractNote={Lead-based metal halide perovskite (MHP) nanocrystals (NCs) have emerged as a promising class of semiconducting nanomaterials for a wide range of optoelectronic and photoelectronic applications. However, the intrinsic lead toxicity of MHP NCs has significantly hampered their large-scale device applications. Copper-base MHP NCs with composition-tunable optical properties have emerged as a prominent lead-free MHP NC candidate. However, comprehensive synthesis space exploration, development, and synthesis science studies of copper-based MHP NCs have been limited by the manual nature of flask-based synthesis and characterization methods. In this study, we present an autonomous approach for the development of lead-free MHP NCs <i>via</i> seamless integration of a modular microfluidic platform with machine learning-assisted NC synthesis modeling and experiment selection to establish a self-driving fluidic lab for accelerated NC synthesis science studies. For the first time, a successful and reproducible in-flow synthesis of Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub> NCs is presented. Autonomous experimentation is then employed for rapid in-flow synthesis science studies of Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub> NCs. The autonomously generated experimental NC synthesis dataset is then utilized for fast-tracked synthetic route optimization of high-performing Cs<sub>3</sub>Cu<sub>2</sub>I<sub>5</sub> NCs.}, journal={Nanoscale}, author={Sadeghi, Sina and Bateni, Fazel and Kim, Taekhoon and Son, Dae Yong and Bennett, Jeffrey A. and Orouji, Negin and Punati, Venkat S. and Stark, Christine and Cerra, Teagan D. and Awad, Rami and et al.}, year={2023}, month={Dec} }
 @article{lópez-guajardo_delgado-licona_álvarez_nigam_montesinos-castellanos_morales-menendez_2021, title={Process intensification 4.0: A new approach for attaining new, sustainable and circular processes enabled by machine learning}, DOI={10.1016/j.cep.2021.108671}, abstractNote={This paper reviews system-level transformations converging into the next generation of Process Intensification strategies defined as PI4.0. Process Intensification 4.0 uses data-driven algorithms to understand other physical and chemical processes that improve equipment design, predictive control, and optimization. Following this, an overview of the use of Artificial Intelligence techniques, particularly Machine Learning for the acceleration of equipment design, process optimization, and streamlining, is presented. This work will highlight and discuss the emerging framework of the integration between Circular Chemistry, Industry 4.0, and Process Intensification and how the data obtained from this integration is at the core of the next generation of Process Intensification strategies. This is supported by a discussion of different cases that apply data-driven models enabled by Machine Learning as a mean to enhance an intensified system (product synthesis, equipment or methods).}, journal={Chemical Engineering and Processing - Process Intensification}, author={López-Guajardo, Enrique A. and Delgado-Licona, Fernando and Álvarez, Alejandro J. and Nigam, Krishna D.P. and Montesinos-Castellanos, Alejandro and Morales-Menendez, Ruben}, year={2021}, month={Oct} }